The ability to quantify and reliably identify color has proven to be illusive. The perception of color has historically proved to be highly subjective. The matter is further complicated by the existence of several ways to specify color. For example, manufacturers of color monitors, video cameras and computer graphics often define color as a combination of the primary colors--red, green and blue (RGB). These additive primaries can be mixed in any combination to produce millions of colors in the visible spectrum. Another traditional color specification method, popular in the publishing industry for mixing inks, is based on combining the subtractive primaries--cyan, yellow, magenta and black (CYMK). Television broadcasting represents color in yet another method; RGB signals are encoded in luminescence (Y) and chrominance (I&Q) signals to facilitate broadcasting.
Recent advancements in optics and microelectronics is tending to expand color imaging into many fields including manufacturing process control, robotics and the like. Today, color is a very important factor in maintaining the overall quality of pharmaceuticals, food products and the like. Because the physical appearance of a product is frequently equated to a perceived if not actual standard of quality, it is incumbent upon a manufacturer to maintain consistency from unit to unit as well as to maintain adherence to specified product standards. By incorporating a color discerning sensor on a line as part of the manufacturing/production process, much more product can be monitored than heretofore was available with recognized off-line measurement techniques, ensuring that only acceptable product is produced. Subjective judgments as to appearance are no longer required whereby continuous product consistency can be achieved.
Although the foregoing concepts are widely recognized and accepted in principle, currently available color sensor technology has a number of deficiencies, limiting the applications thereof in many areas. Present sensors are typically, extremely expensive, bulky and are sensitive to ambient light changes and heat. As a result, sensors are typically employed off-line. Sensors which are used on-line frequently have their sensing heads and processing logic separated by bundles of fiber optic cables wherein the head is positioned to contact or be closely spaced from the target object and the processing portion of the device is physically remote to protect it from the manufacturing process. In the food industry, the problem is reversed in that the goods being produced are extremely susceptible to outside sources of heat and real estate on the production line tends to be extremely limited.
A further limitation of many prior art color sensing devices resides in their extremely short lifetime, requiring frequent shutting down of the production process to replace the sensor's light source or other failed components. Furthermore, because widely accepted prior art approaches at processing color information tend to be laborious, system response time suffers whereby such devices are not suitable for many applications requiring high speed processing.
Although color sensors are frequently employed with powerful data processors, they frequently are incapable of learning; that is, to compare color component information with prior process history to reprogram the system to adjust tolerances, anticipate changing ambient conditions and the like. To the contrary, prior art devices typically compare a simple measured signal with a derived fixed standard. Lastly, such devices also typically have a very limited sensitivity and, although able to differentiate one pure color from another, are incapable of discriminating between slightly varying hues of the same color.